Overview

Dataset statistics

Number of variables21
Number of observations173
Missing cells4
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 KiB
Average record size in memory168.7 B

Variable types

NUM19
CAT2

Warnings

Women is highly correlated with Total and 6 other fieldsHigh correlation
Total is highly correlated with Women and 8 other fieldsHigh correlation
Sample_size is highly correlated with Total and 5 other fieldsHigh correlation
Employed is highly correlated with Total and 8 other fieldsHigh correlation
Full_time is highly correlated with Total and 8 other fieldsHigh correlation
Part_time is highly correlated with Total and 6 other fieldsHigh correlation
Full_time_year_round is highly correlated with Total and 6 other fieldsHigh correlation
Unemployed is highly correlated with Total and 8 other fieldsHigh correlation
P75th is highly correlated with MedianHigh correlation
Median is highly correlated with P75thHigh correlation
Non_college_jobs is highly correlated with Total and 7 other fieldsHigh correlation
Low_wage_jobs is highly correlated with Total and 6 other fieldsHigh correlation
Rank has unique values Unique
Major_code has unique values Unique
Major has unique values Unique
Full_time has unique values Unique
Full_time_year_round has unique values Unique
College_jobs has unique values Unique
Non_college_jobs has unique values Unique
Part_time has 3 (1.7%) zeros Zeros
Unemployed has 5 (2.9%) zeros Zeros
Unemployment_rate has 5 (2.9%) zeros Zeros
Low_wage_jobs has 5 (2.9%) zeros Zeros

Reproduction

Analysis started2022-10-19 13:49:12.637485
Analysis finished2022-10-19 13:50:21.806983
Duration1 minute and 9.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Rank
Real number (ℝ≥0)

UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:21.962446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.6
Q144
median87
Q3130
95-th percentile164.4
Maximum173
Range172
Interquartile range (IQR)86

Descriptive statistics

Standard deviation50.08492787
Coefficient of variation (CV)0.5756888261
Kurtosis-1.2
Mean87
Median Absolute Deviation (MAD)43
Skewness0
Sum15051
Variance2508.5
MonotocityStrictly increasing
2022-10-19T09:50:22.140044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
110.6%
 
12010.6%
 
11210.6%
 
11310.6%
 
11410.6%
 
11510.6%
 
11610.6%
 
11710.6%
 
11810.6%
 
11910.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
110.6%
 
210.6%
 
310.6%
 
410.6%
 
510.6%
 
ValueCountFrequency (%) 
17310.6%
 
17210.6%
 
17110.6%
 
17010.6%
 
16910.6%
 

Major_code
Real number (ℝ≥0)

UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3879.815029
Minimum1100
Maximum6403
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:22.326866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile1301.6
Q12403
median3608
Q35503
95-th percentile6205.4
Maximum6403
Range5303
Interquartile range (IQR)3100

Descriptive statistics

Standard deviation1687.75314
Coefficient of variation (CV)0.435008661
Kurtosis-1.475186702
Mean3879.815029
Median Absolute Deviation (MAD)1400
Skewness0.05549824531
Sum671208
Variance2848510.663
MonotocityNot monotonic
2022-10-19T09:50:22.512365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
241910.6%
 
230510.6%
 
130210.6%
 
110610.6%
 
230010.6%
 
640210.6%
 
260210.6%
 
400110.6%
 
231110.6%
 
611010.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
110010.6%
 
110110.6%
 
110210.6%
 
110310.6%
 
110410.6%
 
ValueCountFrequency (%) 
640310.6%
 
640210.6%
 
629910.6%
 
621210.6%
 
621110.6%
 

Major
Categorical

UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
PETROLEUM ENGINEERING
 
1
MATHEMATICS TEACHER EDUCATION
 
1
FORESTRY
 
1
SOIL SCIENCE
 
1
GENERAL EDUCATION
 
1
Other values (168)
168 
ValueCountFrequency (%) 
PETROLEUM ENGINEERING10.6%
 
MATHEMATICS TEACHER EDUCATION10.6%
 
FORESTRY10.6%
 
SOIL SCIENCE10.6%
 
GENERAL EDUCATION10.6%
 
HISTORY10.6%
 
FRENCH GERMAN LATIN AND OTHER COMMON FOREIGN LANGUAGE STUDIES10.6%
 
INTERCULTURAL AND INTERNATIONAL STUDIES10.6%
 
SOCIAL SCIENCE OR HISTORY TEACHER EDUCATION10.6%
 
COMMUNITY AND PUBLIC HEALTH10.6%
 
Other values (163)16394.2%
 
2022-10-19T09:50:22.728541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique173 ?
Unique (%)100.0%
2022-10-19T09:50:22.921714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length65
Median length24
Mean length25.31213873
Min length5

Total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct172
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean39370.0814
Minimum124
Maximum393735
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:23.102647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile1125.5
Q14549.75
median15104
Q338909.75
95-th percentile188044.4
Maximum393735
Range393611
Interquartile range (IQR)34360

Descriptive statistics

Standard deviation63483.49101
Coefficient of variation (CV)1.612480563
Kurtosis9.379324238
Mean39370.0814
Median Absolute Deviation (MAD)12154.5
Skewness2.87683411
Sum6771654
Variance4030153631
MonotocityNot monotonic
2022-10-19T09:50:23.317660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
233910.6%
 
1423710.6%
 
360710.6%
 
68510.6%
 
14371810.6%
 
14195110.6%
 
4824610.6%
 
2465010.6%
 
2019810.6%
 
1973510.6%
 
Other values (162)16293.6%
 
ValueCountFrequency (%) 
12410.6%
 
60910.6%
 
68510.6%
 
72010.6%
 
75610.6%
 
ValueCountFrequency (%) 
39373510.6%
 
32992710.6%
 
28070910.6%
 
23459010.6%
 
21399610.6%
 

Men
Real number (ℝ≥0)

Distinct172
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16723.40698
Minimum119
Maximum173809
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:23.503114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile508.25
Q12177.5
median5434
Q314631
95-th percentile83167.6
Maximum173809
Range173690
Interquartile range (IQR)12453.5

Descriptive statistics

Standard deviation28122.43347
Coefficient of variation (CV)1.681621066
Kurtosis8.788483299
Mean16723.40698
Median Absolute Deviation (MAD)4431
Skewness2.840798729
Sum2876426
Variance790871264.5
MonotocityNot monotonic
2022-10-19T09:50:23.686562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
205710.6%
 
387210.6%
 
315610.6%
 
47610.6%
 
2689310.6%
 
7825310.6%
 
1283510.6%
 
857510.6%
 
995010.6%
 
410310.6%
 
Other values (162)16293.6%
 
ValueCountFrequency (%) 
11910.6%
 
12410.6%
 
13410.6%
 
28010.6%
 
40410.6%
 
ValueCountFrequency (%) 
17380910.6%
 
13223810.6%
 
11503010.6%
 
11176210.6%
 
9974310.6%
 

Women
Real number (ℝ≥0)

HIGH CORRELATION

Distinct171
Distinct (%)99.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean22646.67442
Minimum0
Maximum307087
Zeros1
Zeros (%)0.6%
Memory size1.4 KiB
2022-10-19T09:50:24.054881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile330.95
Q11778.25
median8386.5
Q322553.75
95-th percentile109833.95
Maximum307087
Range307087
Interquartile range (IQR)20775.5

Descriptive statistics

Standard deviation41057.33074
Coefficient of variation (CV)1.81295187
Kurtosis16.39671616
Mean22646.67442
Median Absolute Deviation (MAD)7113.5
Skewness3.593272952
Sum3895228
Variance1685704407
MonotocityNot monotonic
2022-10-19T09:50:24.225831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
97321.2%
 
1036510.6%
 
45110.6%
 
20910.6%
 
11682510.6%
 
6369810.6%
 
3541110.6%
 
1607510.6%
 
1024810.6%
 
1563210.6%
 
Other values (161)16193.1%
 
ValueCountFrequency (%) 
010.6%
 
7710.6%
 
10910.6%
 
13110.6%
 
13510.6%
 
ValueCountFrequency (%) 
30708710.6%
 
18762110.6%
 
16894710.6%
 
15783310.6%
 
15611810.6%
 

Major_category
Categorical

Distinct16
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Engineering
29 
Education
16 
Humanities & Liberal Arts
15 
Biology & Life Science
14 
Business
13 
Other values (11)
86 
ValueCountFrequency (%) 
Engineering2916.8%
 
Education169.2%
 
Humanities & Liberal Arts158.7%
 
Biology & Life Science148.1%
 
Business137.5%
 
Health126.9%
 
Computers & Mathematics116.4%
 
Physical Sciences105.8%
 
Agriculture & Natural Resources105.8%
 
Social Science95.2%
 
Other values (6)3419.7%
 
2022-10-19T09:50:24.399930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.6%
2022-10-19T09:50:24.546982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length14
Mean length16.7283237
Min length4

ShareWomen
Real number (ℝ≥0)

Distinct172
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.522223365
Minimum0
Maximum0.968953683
Zeros1
Zeros (%)0.6%
Memory size1.4 KiB
2022-10-19T09:50:24.711290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1331516353
Q10.3360262068
median0.534024037
Q30.7032992108
95-th percentile0.8879201835
Maximum0.968953683
Range0.968953683
Interquartile range (IQR)0.367273004

Descriptive statistics

Standard deviation0.2312049857
Coefficient of variation (CV)0.4427319826
Kurtosis-0.9224765386
Mean0.522223365
Median Absolute Deviation (MAD)0.186895961
Skewness-0.1346841674
Sum89.82241877
Variance0.05345574542
MonotocityNot monotonic
2022-10-19T09:50:24.894342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.12056434410.6%
 
0.72803259110.6%
 
0.12503465510.6%
 
0.30510948910.6%
 
0.81287660610.6%
 
0.44873230910.6%
 
0.73396758310.6%
 
0.65212981710.6%
 
0.50737696810.6%
 
0.79209526210.6%
 
Other values (162)16293.6%
 
ValueCountFrequency (%) 
010.6%
 
0.07745302710.6%
 
0.09071250910.6%
 
0.10185185210.6%
 
0.10731319610.6%
 
ValueCountFrequency (%) 
0.96895368310.6%
 
0.96799811910.6%
 
0.92780724610.6%
 
0.92374547910.6%
 
0.9109325710.6%
 

Sample_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct147
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.0809249
Minimum2
Maximum4212
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:25.082880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q139
median130
Q3338
95-th percentile1668.6
Maximum4212
Range4210
Interquartile range (IQR)299

Descriptive statistics

Standard deviation618.3610223
Coefficient of variation (CV)1.736574411
Kurtosis11.9861795
Mean356.0809249
Median Absolute Deviation (MAD)105
Skewness3.192037349
Sum61602
Variance382370.3539
MonotocityNot monotonic
2022-10-19T09:50:25.251822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3631.7%
 
431.7%
 
731.7%
 
2231.7%
 
42521.2%
 
9521.2%
 
3721.2%
 
15821.2%
 
14221.2%
 
2421.2%
 
Other values (137)14986.1%
 
ValueCountFrequency (%) 
210.6%
 
321.2%
 
431.7%
 
521.2%
 
731.7%
 
ValueCountFrequency (%) 
421210.6%
 
268410.6%
 
258410.6%
 
255410.6%
 
239410.6%
 

Employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct171
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31192.76301
Minimum0
Maximum307933
Zeros1
Zeros (%)0.6%
Memory size1.4 KiB
2022-10-19T09:50:25.432037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile751.6
Q13608
median11797
Q331433
95-th percentile149243.6
Maximum307933
Range307933
Interquartile range (IQR)27825

Descriptive statistics

Standard deviation50675.00224
Coefficient of variation (CV)1.624575618
Kurtosis9.194017561
Mean31192.76301
Median Absolute Deviation (MAD)9348
Skewness2.863443441
Sum5396348
Variance2567955852
MonotocityNot monotonic
2022-10-19T09:50:25.632017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
212521.2%
 
705221.2%
 
197610.6%
 
1770010.6%
 
343110.6%
 
300710.6%
 
61310.6%
 
11824110.6%
 
10564610.6%
 
3831510.6%
 
Other values (161)16193.1%
 
ValueCountFrequency (%) 
010.6%
 
55910.6%
 
60410.6%
 
61310.6%
 
64010.6%
 
ValueCountFrequency (%) 
30793310.6%
 
27623410.6%
 
19018310.6%
 
18229510.6%
 
18090310.6%
 

Full_time
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26029.30636
Minimum111
Maximum251540
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:25.806049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile702.6
Q13154
median10048
Q325147
95-th percentile129074.6
Maximum251540
Range251429
Interquartile range (IQR)21993

Descriptive statistics

Standard deviation42869.65509
Coefficient of variation (CV)1.64697647
Kurtosis8.765504957
Mean26029.30636
Median Absolute Deviation (MAD)8031
Skewness2.843483682
Sum4503070
Variance1837807328
MonotocityNot monotonic
2022-10-19T09:50:25.984787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
184910.6%
 
1125910.6%
 
247310.6%
 
48810.6%
 
9840810.6%
 
8468110.6%
 
2934010.6%
 
1435410.6%
 
1400210.6%
 
1009910.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
11110.6%
 
48810.6%
 
52410.6%
 
55610.6%
 
55810.6%
 
ValueCountFrequency (%) 
25154010.6%
 
23320510.6%
 
17138510.6%
 
15666810.6%
 
15196710.6%
 

Part_time
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8832.398844
Minimum0
Maximum115172
Zeros3
Zeros (%)1.7%
Memory size1.4 KiB
2022-10-19T09:50:26.159710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile179
Q11030
median3299
Q39948
95-th percentile38185
Maximum115172
Range115172
Interquartile range (IQR)8918

Descriptive statistics

Standard deviation14648.17947
Coefficient of variation (CV)1.658459919
Kurtosis18.30512781
Mean8832.398844
Median Absolute Deviation (MAD)2767
Skewness3.621007516
Sum1528005
Variance214569161.9
MonotocityNot monotonic
2022-10-19T09:50:26.334340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031.7%
 
84721.2%
 
27010.6%
 
637710.6%
 
89110.6%
 
18510.6%
 
2955810.6%
 
4065710.6%
 
1456910.6%
 
797810.6%
 
Other values (160)16092.5%
 
ValueCountFrequency (%) 
031.7%
 
12610.6%
 
13310.6%
 
13510.6%
 
15010.6%
 
ValueCountFrequency (%) 
11517210.6%
 
7237110.6%
 
5782510.6%
 
5035710.6%
 
4988910.6%
 

Full_time_year_round
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19694.42775
Minimum111
Maximum199897
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:26.550687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile538.6
Q12453
median7413
Q316891
95-th percentile93263.2
Maximum199897
Range199786
Interquartile range (IQR)14438

Descriptive statistics

Standard deviation33160.94151
Coefficient of variation (CV)1.683772788
Kurtosis9.350771185
Mean19694.42775
Median Absolute Deviation (MAD)5883
Skewness2.92906522
Sum3407136
Variance1099648042
MonotocityNot monotonic
2022-10-19T09:50:26.730162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120710.6%
 
807310.6%
 
176310.6%
 
38310.6%
 
7353110.6%
 
5921810.6%
 
2005610.6%
 
880110.6%
 
887110.6%
 
746010.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
11110.6%
 
34010.6%
 
38310.6%
 
38810.6%
 
39110.6%
 
ValueCountFrequency (%) 
19989710.6%
 
17443810.6%
 
13829910.6%
 
12723010.6%
 
12316910.6%
 

Unemployed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct161
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2416.32948
Minimum0
Maximum28169
Zeros5
Zeros (%)2.9%
Memory size1.4 KiB
2022-10-19T09:50:27.068538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1304
median893
Q32393
95-th percentile11536.4
Maximum28169
Range28169
Interquartile range (IQR)2089

Descriptive statistics

Standard deviation4112.803148
Coefficient of variation (CV)1.702087063
Kurtosis12.27882716
Mean2416.32948
Median Absolute Deviation (MAD)756
Skewness3.174630334
Sum418025
Variance16915149.73
MonotocityNot monotonic
2022-10-19T09:50:27.230720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
052.9%
 
8721.2%
 
75721.2%
 
106721.2%
 
30821.2%
 
3321.2%
 
41921.2%
 
28621.2%
 
7821.2%
 
21610.6%
 
Other values (151)15187.3%
 
ValueCountFrequency (%) 
052.9%
 
1110.6%
 
1610.6%
 
2310.6%
 
3321.2%
 
ValueCountFrequency (%) 
2816910.6%
 
2150210.6%
 
1502210.6%
 
1494610.6%
 
1460210.6%
 

Unemployment_rate
Real number (ℝ≥0)

ZEROS

Distinct169
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06819083091
Minimum0
Maximum0.177226407
Zeros5
Zeros (%)2.9%
Memory size1.4 KiB
2022-10-19T09:50:27.410009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0191376886
Q10.050306435
median0.067960766
Q30.087557114
95-th percentile0.1133826206
Maximum0.177226407
Range0.177226407
Interquartile range (IQR)0.037250679

Descriptive statistics

Standard deviation0.0303309398
Coefficient of variation (CV)0.4447949877
Kurtosis1.007472309
Mean0.06819083091
Median Absolute Deviation (MAD)0.0183129
Skewness0.2956186683
Sum11.79701375
Variance0.0009199659091
MonotocityNot monotonic
2022-10-19T09:50:27.621541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
052.9%
 
0.01838052710.6%
 
0.01620283510.6%
 
0.02222855510.6%
 
0.09672574310.6%
 
0.05735992910.6%
 
0.09566691210.6%
 
0.07556638610.6%
 
0.08363353110.6%
 
0.05408294110.6%
 
Other values (159)15991.9%
 
ValueCountFrequency (%) 
052.9%
 
0.00633434310.6%
 
0.01168969210.6%
 
0.01620283510.6%
 
0.01838052710.6%
 
ValueCountFrequency (%) 
0.17722640710.6%
 
0.159490610.6%
 
0.15184980710.6%
 
0.14904819810.6%
 
0.12842629910.6%
 

Median
Real number (ℝ≥0)

HIGH CORRELATION

Distinct59
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40151.44509
Minimum22000
Maximum110000
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:27.814637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22000
5-th percentile28000
Q133000
median36000
Q345000
95-th percentile60000
Maximum110000
Range88000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation11470.1818
Coefficient of variation (CV)0.2856729509
Kurtosis7.649279215
Mean40151.44509
Median Absolute Deviation (MAD)4000
Skewness2.036867702
Sum6946200
Variance131565070.6
MonotocityDecreasing
2022-10-19T09:50:27.983323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
350002011.6%
 
40000179.8%
 
5000095.2%
 
3300095.2%
 
4500095.2%
 
3400084.6%
 
3200084.6%
 
3000084.6%
 
3600063.5%
 
6000063.5%
 
Other values (49)7342.2%
 
ValueCountFrequency (%) 
2200010.6%
 
2340010.6%
 
2500021.2%
 
2600010.6%
 
2700021.2%
 
ValueCountFrequency (%) 
11000010.6%
 
7500010.6%
 
7300010.6%
 
7000010.6%
 
6500021.2%
 

P25th
Real number (ℝ≥0)

Distinct48
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29501.44509
Minimum18500
Maximum95000
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:28.192432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18500
5-th percentile20000
Q124000
median27000
Q333000
95-th percentile45000
Maximum95000
Range76500
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation9166.005235
Coefficient of variation (CV)0.3106968221
Kurtosis14.51491312
Mean29501.44509
Median Absolute Deviation (MAD)4000
Skewness2.728044969
Sum5103750
Variance84015651.97
MonotocityNot monotonic
2022-10-19T09:50:28.364075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
250002212.7%
 
300001810.4%
 
24000126.9%
 
20000116.4%
 
23000105.8%
 
2700074.0%
 
3500063.5%
 
2200052.9%
 
2600052.9%
 
2800052.9%
 
Other values (38)7241.6%
 
ValueCountFrequency (%) 
1850010.6%
 
1920031.7%
 
20000116.4%
 
2080010.6%
 
2100042.3%
 
ValueCountFrequency (%) 
9500010.6%
 
5500010.6%
 
5300010.6%
 
5000042.3%
 
4800010.6%
 

P75th
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51494.21965
Minimum22000
Maximum125000
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2022-10-19T09:50:28.546109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22000
5-th percentile35600
Q142000
median47000
Q360000
95-th percentile75400
Maximum125000
Range103000
Interquartile range (IQR)18000

Descriptive statistics

Standard deviation14906.27974
Coefficient of variation (CV)0.2894748156
Kurtosis4.97931887
Mean51494.21965
Median Absolute Deviation (MAD)7000
Skewness1.816489279
Sum8908500
Variance222197175.7
MonotocityNot monotonic
2022-10-19T09:50:28.732894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
450001810.4%
 
500001810.4%
 
40000179.8%
 
42000116.4%
 
60000116.4%
 
7000063.5%
 
4100063.5%
 
6500063.5%
 
3500063.5%
 
3800052.9%
 
Other values (44)6939.9%
 
ValueCountFrequency (%) 
2200010.6%
 
2600010.6%
 
3400010.6%
 
3500063.5%
 
3600010.6%
 
ValueCountFrequency (%) 
12500010.6%
 
10900010.6%
 
10500010.6%
 
10200010.6%
 
9000021.2%
 

College_jobs
Real number (ℝ≥0)

UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12322.63584
Minimum0
Maximum151643
Zeros1
Zeros (%)0.6%
Memory size1.4 KiB
2022-10-19T09:50:28.945354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile444.8
Q11675
median4390
Q314444
95-th percentile42789.4
Maximum151643
Range151643
Interquartile range (IQR)12769

Descriptive statistics

Standard deviation21299.86886
Coefficient of variation (CV)1.728515647
Kurtosis17.48266034
Mean12322.63584
Median Absolute Deviation (MAD)3646
Skewness3.77129362
Sum2131816
Variance453684413.6
MonotocityNot monotonic
2022-10-19T09:50:29.117290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
153410.6%
 
1069910.6%
 
109610.6%
 
35510.6%
 
8200710.6%
 
3533610.6%
 
1505110.6%
 
495610.6%
 
1092810.6%
 
522510.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
010.6%
 
16210.6%
 
22110.6%
 
28810.6%
 
34610.6%
 
ValueCountFrequency (%) 
15164310.6%
 
12514810.6%
 
10808510.6%
 
8823210.6%
 
8200710.6%
 

Non_college_jobs
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13284.49711
Minimum0
Maximum148395
Zeros1
Zeros (%)0.6%
Memory size1.4 KiB
2022-10-19T09:50:29.303665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile242.6
Q11591
median4595
Q311783
95-th percentile65146.4
Maximum148395
Range148395
Interquartile range (IQR)10192

Descriptive statistics

Standard deviation23789.65536
Coefficient of variation (CV)1.790783284
Kurtosis12.90542129
Mean13284.49711
Median Absolute Deviation (MAD)3917
Skewness3.377170775
Sum2298218
Variance565947702.3
MonotocityNot monotonic
2022-10-19T09:50:29.496731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
36410.6%
 
197710.6%
 
169210.6%
 
14410.6%
 
3111210.6%
 
5456910.6%
 
1819310.6%
 
1034310.6%
 
556110.6%
 
738510.6%
 
Other values (163)16394.2%
 
ValueCountFrequency (%) 
010.6%
 
5010.6%
 
6710.6%
 
10210.6%
 
14410.6%
 
ValueCountFrequency (%) 
14839510.6%
 
14186010.6%
 
10083110.6%
 
9796410.6%
 
9388910.6%
 

Low_wage_jobs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct166
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3859.017341
Minimum0
Maximum48207
Zeros5
Zeros (%)2.9%
Memory size1.4 KiB
2022-10-19T09:50:29.682028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.6
Q1340
median1231
Q33466
95-th percentile17465
Maximum48207
Range48207
Interquartile range (IQR)3126

Descriptive statistics

Standard deviation6944.998579
Coefficient of variation (CV)1.799680583
Kurtosis13.32662508
Mean3859.017341
Median Absolute Deviation (MAD)1039
Skewness3.338072027
Sum667610
Variance48233005.26
MonotocityNot monotonic
2022-10-19T09:50:29.862033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
052.9%
 
8121.2%
 
26321.2%
 
30821.2%
 
1144310.6%
 
1683910.6%
 
526710.6%
 
316810.6%
 
180610.6%
 
185410.6%
 
Other values (156)15690.2%
 
ValueCountFrequency (%) 
052.9%
 
2510.6%
 
3110.6%
 
3710.6%
 
4910.6%
 
ValueCountFrequency (%) 
4820710.6%
 
3239510.6%
 
2833910.6%
 
2796810.6%
 
2744010.6%
 

Interactions

2022-10-19T09:49:21.915752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:22.220936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:22.390051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:22.706226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:23.131001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:23.315462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:23.482474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:23.639593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:23.861107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:24.093334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:24.428926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:24.727683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:24.879842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:25.046198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:25.182456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:25.710035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:25.877677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:26.065242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:26.402547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:26.569871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:26.870673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:27.057932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:27.331069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:27.546626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:27.726672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:27.867809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:28.013350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:28.203268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:28.341719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:28.629517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:28.849490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:29.327583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:29.498508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:29.620777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:29.769437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:29.924226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:30.176750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:30.361899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:30.562662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:30.739075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:30.907052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:31.047873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:31.256112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:31.462352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:31.717109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:31.893013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.085597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.237939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.447918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.608025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.744061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:32.880676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.013320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.184105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.372886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.543505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.758076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:33.909799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:34.282948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:34.426283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:34.644084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:34.787715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:34.919968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.121911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.278459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.422086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.581038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.709707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:35.877696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:36.081987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:36.336719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:36.487025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:36.677646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:36.854881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.066217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.244223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.412872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.552750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.717883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:37.880668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.025006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.172477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.325458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.471727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.643980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:38.968710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.151454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.314208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.431372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.568294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.708399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.840122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:39.963513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.123273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.258178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.390168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.516147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.632861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.763166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:40.884541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:49:41.009369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-19T09:50:11.587270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:11.735012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:11.867772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:12.031598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:12.825409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:12.979318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.120842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.248661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.389192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.512640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.660760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.825237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:13.966578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.102415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.234640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.403803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.529633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.659008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.791289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:14.921163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.053116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.215912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.344768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.471181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.604710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.740141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.867600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:15.990415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:16.117370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:16.280723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:16.479548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:16.823422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:16.983941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.107366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.240670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.363688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.516181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.647005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.781276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:17.917727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.042075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.163196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.278136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.401672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.527056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.643580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.758708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:18.872438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.016982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.136192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.253725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.380650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.495527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.648013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.772365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:19.894135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:20.011858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:20.132319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:20.427688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-19T09:50:30.191286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-19T09:50:30.482986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-19T09:50:30.766776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-19T09:50:31.077003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-19T09:50:20.710145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:21.177292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:21.449045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T09:50:21.586533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sample

First rows

RankMajor_codeMajorTotalMenWomenMajor_categoryShareWomenSample_sizeEmployedFull_timePart_timeFull_time_year_roundUnemployedUnemployment_rateMedianP25thP75thCollege_jobsNon_college_jobsLow_wage_jobs
012419PETROLEUM ENGINEERING2339.02057.0282.0Engineering0.12056436197618492701207370.018381110000950001250001534364193
122416MINING AND MINERAL ENGINEERING756.0679.077.0Engineering0.1018527640556170388850.11724175000550009000035025750
232415METALLURGICAL ENGINEERING856.0725.0131.0Engineering0.1530373648558133340160.02409673000500001050004561760
342417NAVAL ARCHITECTURE AND MARINE ENGINEERING1258.01123.0135.0Engineering0.107313167581069150692400.0501257000043000800005291020
452405CHEMICAL ENGINEERING32260.021239.011021.0Engineering0.341631289256942317051801669716720.061098650005000075000183144440972
562418NUCLEAR ENGINEERING2573.02200.0373.0Engineering0.144967171857203826414494000.17722665000500001020001142657244
676202ACTUARIAL SCIENCE3777.02110.01667.0Business0.441356512912292429624823080.0956526200053000720001768314259
785001ASTRONOMY AND ASTROPHYSICS1792.0832.0960.0Physical Sciences0.5357141015261085553827330.0211676200031500109000972500220
892414MECHANICAL ENGINEERING91227.080320.010907.0Engineering0.11955910297644271298131015463946500.05734260000480007000052844163843253
9102408ELECTRICAL ENGINEERING81527.065511.016016.0Engineering0.1964506316192855450126954141338950.05917460000450007200045829108743170

Last rows

RankMajor_codeMajorTotalMenWomenMajor_categoryShareWomenSample_sizeEmployedFull_timePart_timeFull_time_year_roundUnemployedUnemployment_rateMedianP25thP75thCollege_jobsNon_college_jobsLow_wage_jobs
1631646102COMMUNICATION DISORDERS SCIENCES AND SERVICES38279.01225.037054.0Health0.967998952976319975138621446014870.0475842800020000400001995794045125
1641652307EARLY CHILDHOOD EDUCATION37589.01167.036422.0Education0.968954342325512756970012074813600.0401052800021000350002351577052868
1651662603OTHER FOREIGN LANGUAGES11204.03472.07732.0Humanities & Liberal Arts0.6901115670525197368532148460.107116275002290038000232637031115
1661676001DRAMA AND THEATER ARTS43249.014440.028809.0Arts0.6661193573616525147159941689130400.07754127000192003500069942531311068
1671683302COMPOSITION AND RHETORIC18953.07022.011931.0Humanities & Liberal Arts0.62950515115053101216612783213400.081742270002000035000485581003466
1681693609ZOOLOGY8409.03050.05359.0Biology & Life Science0.6372934762595043219036023040.04632026000200003900027712947743
1691705201EDUCATIONAL PSYCHOLOGY2854.0522.02332.0Psychology & Social Work0.81709972125184857212111480.065112250002400034000148861582
1701715202CLINICAL PSYCHOLOGY2838.0568.02270.0Psychology & Social Work0.799859132101172464812933680.149048250002500040000986870622
1711725203COUNSELING PSYCHOLOGY4626.0931.03695.0Psychology & Social Work0.798746213777315496527382140.05362123400192002600024031245308
1721733501LIBRARY SCIENCE1098.0134.0964.0Education0.8779602742593237410870.104946220002000022000288338192